Sensitivity analysis of retrovirus HTLV-1 transactivation

Alberto Corradin, Barbara Di Camillo, Vincenzo Ciminale, Gianna Toffolo, Claudio Cobelli

Research output: Contribution to journalArticlepeer-review


Human T-cell leukemia virus type 1 is a human retrovirus endemic in many areas of the world. Although many studies indicated a key role of the viral protein Tax in the control of viral transcription, the mechanisms controlling HTLV-1 expression and its persistence in vivo are still poorly understood. To assess Tax effects on viral kinetics, we developed a HTLV-1 model. Two parameters that capture both its deterministic and stochastic behavior were quantified: Tax signal-to-noise ratio (SNR), which measures the effect of stochastic phenomena on Tax expression as the ratio between the protein steady-state level and the variance of the noise causing fluctuations around this value; t 1/2, a parameter representative of the duration of Tax transient expression pulses, that is, of Tax bursts due to stochastic phenomena. Sensitivity analysis indicates that the major determinant of Tax SNR is the transactivation constant, the system parameter weighting the enhancement of retrovirus transcription due to transactivation. In contrast, t 1/2 is strongly influenced by the degradation rate of the mRNA. In addition to shedding light into the mechanism of Tax transactivation, the obtained results are of potential interest for novel drug development strategies since the two parameters most affecting Tax transactivation can be experimentally tuned, e.g. by perturbing protein phosphorylation and by RNA interference.

Original languageEnglish
Pages (from-to)183-193
Number of pages11
JournalJournal of Computational Biology
Issue number2
Publication statusPublished - Feb 1 2011


  • Gillespie's exact stochastic simulations
  • HTLV-1
  • sensitivity analysis
  • stochasticity in gene circuits
  • transactivation

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Modelling and Simulation
  • Computational Theory and Mathematics

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